e-Prime - Advances in Electrical Engineering, Electronics and Energy最新文献_第2页

Online estimation of wideband output impedance and control parameters of single-phase inverters using pseudo-random perturbation 基于伪随机摄动的单相逆变器宽带输出阻抗和控制参数在线估计

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-10-14 DOI: 10.1016/j.prime.2025.101126

Ian Paul Gerber, Fredrick Mukundi Mwaniki, Hendrik Johannes Vermeulen

The growing deployment of single-phase inverters in residential low-voltage distribution networks poses new challenges to system stability and power quality. Accurate simulation models are essential for analysing these effects and enabling scenario assessment without costly and time-consuming physical testing. Wideband inverter models, in particular, are critical for capturing the inverter’s dynamic behaviour across a broad frequency range. The inverter’s output impedance profile plays a key role in identifying internal parameters, such as filter and control settings, typically not disclosed by manufacturers, and supports impedance-based stability analysis. This paper presents a methodology for online estimating an inverter’s wideband output impedance and internal control parameters. A pseudo-random impulse sequence is injected into the inverter AC terminals in situ to perturb the system, from which the output impedance is estimated. A case study on a standalone single-phase inverter supplying

2.6 A_{RMS}

demonstrates a strong correlation between the experimentally derived impedance and its analytical counterpart. The inverter’s impedance frequency response and time-domain output signals are further analysed to extract controller parameters using a three-step estimation process based on particle swarm optimisation. The approach is validated through both simulation and experimental results, confirming its accuracy and effectiveness in parameter identification.

随着住宅低压配电网中单相逆变器的日益普及，对系统稳定性和电能质量提出了新的挑战。准确的模拟模型对于分析这些影响和在不进行昂贵和耗时的物理测试的情况下进行情景评估至关重要。特别是宽带逆变器模型，对于捕获逆变器在宽频率范围内的动态行为至关重要。逆变器的输出阻抗配置文件在识别内部参数（如滤波器和控制设置）方面起着关键作用，通常不被制造商披露，并支持基于阻抗的稳定性分析。本文提出了一种在线估计逆变器宽带输出阻抗和内部控制参数的方法。在逆变器交流端注入伪随机脉冲序列，对系统进行扰动，估计输出阻抗。对提供2.6ARMS的独立单相逆变器的案例研究表明，实验推导的阻抗与其分析对应阻抗之间存在很强的相关性。进一步分析逆变器的阻抗频率响应和时域输出信号，采用基于粒子群优化的三步估计过程提取控制器参数。仿真和实验结果验证了该方法在参数识别方面的准确性和有效性。

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引用次数: 0

Optimization of pico-scale Turgo turbines for rural electrification: Design, performance, and applications in decentralized energy systems 农村电气化微尺度涡轮的优化：设计、性能和分散式能源系统的应用

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-10-12 DOI: 10.1016/j.prime.2025.101127

Dendy Adanta , Dewi Puspita Sari , Imam Syofii , Muhammat Risky Ramadan , Amir Arifin , Ahmad Fudholi

Indonesia’s energy transition necessitates decentralized solutions to address the electrification gap and reduce fossil fuel dependence. This study optimizes a pico-scale Turgo turbine for low-head hydropower generation by revising the traditional design ratio derived from Pelton turbine. Experimental testing of a 3D-printed prototype under controlled conditions (3 m head, 44 L per minute flow) combines velocity triangle analysis with response surface methodology to evaluate runner and blade geometries. Results derived from that adjusting the conventional size ratio improves efficiency, with a 23 cm runner and 4 cm blade achieving a peak efficiency of 19.95 % at optimal rotation. A predictive polynomial model shows diminishing returns with larger components. This optimized design offers a practical solution for remote communities, potentially replacing diesel generators while reducing costs and environmental impact. Although material and scalability limitations require further investigation, this study provides actionable guidance for small-scale hydropower systems, supporting Indonesia's renewable energy goals and global sustainable electrification efforts.

印度尼西亚的能源转型需要分散的解决方案来解决电气化差距和减少对化石燃料的依赖。本文通过对传统水轮机设计比的修正，对一种用于低水头水力发电的微型涡轮进行了优化设计。在受控条件下（3米水头，每分钟44升流量），3d打印原型的实验测试将速度三角形分析与响应面方法相结合，以评估转轮和叶片的几何形状。结果表明，调整常规尺寸比可以提高效率，在最佳转速下，23 cm流道和4 cm叶片的效率最高可达19.95%。预测多项式模型显示，随着成分的增加，收益递减。这种优化设计为偏远社区提供了一种实用的解决方案，有可能取代柴油发电机，同时降低成本和对环境的影响。虽然材料和可扩展性的限制需要进一步调查，但本研究为小型水电系统提供了可操作的指导，支持印度尼西亚的可再生能源目标和全球可持续电气化努力。

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引用次数: 0

Leveraging IoT, digital twin and machine learning for smart energy audit in office building: a systematic literature review and recommendations 利用物联网、数字孪生和机器学习进行办公大楼智能能源审计：系统的文献综述和建议

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-10-09 DOI: 10.1016/j.prime.2025.101124

Ali Zaenal Abidin , I Ketut Agung Enriko , Aloysius Adya Pramudita

Energy audits play a pivotal role in improving energy efficiency and reducing carbon emissions in office buildings. However, conventional audits often suffer from fragmented insights, lack of system-level monitoring, establishing energy baseline, and insufficient incorporation of occupant behavior. To address these challenges, this study conducts a systematic literature review of recent applications of Internet of Things (IoT), machine learning (ML), and digital twin (DT) technologies in the energy audit domain. The review, guided by PRISMA methodology, analyzes eleven selected studies published between 2022 and 2024, revealing that while ML dominates in predictive modeling, IoT and DT remain underutilized in delivering integrated, efficiency recommendations. The analysis identifies three key engineering gaps: limited use of occupant behavior data, absence of continuous energy baseline modeling, and lack of systems capable of generating real-time efficiency recommendations. In response, this paper proposes a novel AIoT-based energy audit framework that combines real-time monitoring via IoT with ML-driven analytics and optimization, supported optionally by DT-based simulation. The proposed framework aims to enable continuous, system-level audits aligned with ISO 50000 standards, offering practical pathways for building managers to diagnose inefficiencies and implement energy-saving actions. Validating the model in real-world office environments, expanding input variables, and integration strategy with building automation systems are further important study to realize intelligent and scalable energy audit solutions.

能源审计在提高办公建筑的能源效率和减少碳排放方面发挥着关键作用。然而，传统的审计往往受到分散的见解、缺乏系统级监控、建立能源基线和对居住者行为的不充分结合的影响。为了应对这些挑战，本研究对物联网（IoT）、机器学习（ML）和数字孪生（DT）技术在能源审计领域的最新应用进行了系统的文献综述。该评估以PRISMA方法为指导，分析了2022年至2024年间发表的11项精选研究，结果显示，虽然机器学习在预测建模中占据主导地位，但物联网和DT在提供综合效率建议方面仍未得到充分利用。分析指出了三个关键的工程缺陷：乘员行为数据的有限使用，缺乏连续的能源基线建模，以及缺乏能够生成实时效率建议的系统。作为回应，本文提出了一种新的基于人工智能的能源审计框架，该框架将通过物联网进行的实时监控与机器学习驱动的分析和优化相结合，并可选择支持基于机器学习的模拟。拟议的框架旨在实现与ISO 50000标准一致的持续系统级审计，为建筑管理人员提供诊断效率低下和实施节能行动的实用途径。在真实办公环境中验证模型、扩展输入变量以及与楼宇自动化系统集成策略是实现智能和可扩展的能源审计解决方案的进一步重要研究。

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引用次数: 0

Analytic and simulation results of a Gaussian analog random constant based on resistance dispersion 基于电阻色散的高斯模拟随机常数的分析与仿真结果

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-10-08 DOI: 10.1016/j.prime.2025.101121

Riccardo Bernardini

Physically Unclonable Constants (PUCs) are a special type of Physically Unclonable Functions (PUFs) and they can be used to embed secret bit-strings in chips. Most PUCs are an array of cells where each cell is a digital circuit that evolve spontaneously toward one of two states, the chosen state being function of random manufacturing process variations. In this paper we propose a building block for new PUF/PUC that we call Analog Random Constant (ARC). The output of an ARC is an analog value randomly selected at manufacturing time. An ARC can be used to build a PUF/PUC by digitizing its output and suitably processing the digital value. The ratio behind this approach is that the ARC output has the potential of providing several random bits, reducing the required footprint. Preliminary theoretical analysis and simulation results are presented. The proposed APUC has interesting performances (e.g., it can provide up to 5 bits per cell) that grant for further investigation.

物理不可克隆常数（physical unclable Constants, PUCs）是物理不可克隆函数（physical unclable Functions, puf）的一种特殊类型，可用于在芯片中嵌入秘密比特串。大多数PUCs是一组单元，其中每个单元是一个数字电路，自发地向两种状态之一进化，所选择的状态是随机制造过程变化的函数。在本文中，我们提出了一个新的PUF/PUC的构建块，我们称之为模拟随机常数（ARC）。ARC的输出是在制造时随机选择的模拟值。ARC可以通过对其输出进行数字化并对数字值进行适当处理来构建PUF/PUC。这种方法背后的比率是ARC输出具有提供几个随机位的潜力，从而减少所需的占用空间。给出了初步的理论分析和仿真结果。所提出的APUC具有有趣的性能（例如，它可以为每个单元提供多达5位），可以进行进一步的研究。

引用次数: 0

Overview of key power system state estimation methods with a focus on artificial intelligence-based approaches 概述电力系统状态估计的关键方法，重点是基于人工智能的方法

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-10-08 DOI: 10.1016/j.prime.2025.101125

Mohammad Amin Ranjbar , Sasan Azad , Morteza Nazari-Heris , Mostafa Mohammadpourfard

State estimation (SE) is the most critical part of power systems management and control centers because correct data from the equipment in the network is needed before any operation. Power systems in the past were less complex than today's systems, so simple methods were sufficient to solve the SE problem. As power systems have developed and distribution systems have become more interconnected to enhance reliability and handle growing loads and uncertainties, the methods for solving the SE problem have evolved over time. Many methods have been employed so far to address the state estimate problem; each of these techniques has the potential to be helpful in particular situations; therefore, identifying the strategies and getting familiar with their features can be crucial. Based on this issue, SE methods are divided into two categories. In one category, their dynamic and static characteristics are specified, and another category is based on the application of methods. Most of the methods have been studied, and the advantages and disadvantages of each have been thoroughly investigated to identify their strengths and weaknesses. The methods based on artificial intelligence (AI) can have good potential in solving SE problems, so they have been specifically investigated. This category of SE methods can be beneficial in solving future problems. Based on this, the existing challenges for the future of SE have been discussed. As a case study, we demonstrate how AI techniques, such as transfer learning (TL), can address one of these challenges—specifically in handling network reconfiguration in a 118-bus system. This example can guide those interested in the field to tackle similar challenges and provide direction for future research.

状态估计是电力系统管理和控制中心中最关键的部分，因为在运行之前需要从电网中获取正确的设备数据。过去的电力系统没有今天的系统那么复杂，所以简单的方法就足以解决SE问题。随着电力系统的发展，配电系统变得更加互联，以提高可靠性，处理不断增长的负荷和不确定性，解决SE问题的方法也随着时间的推移而发展。到目前为止，已经采用了许多方法来解决状态估计问题；这些技术中的每一种都有可能在特定情况下有所帮助；因此，确定策略并熟悉它们的特征是至关重要的。基于这个问题，SE方法分为两类。一类是指定了它们的动态和静态特性，另一类是基于方法的应用。大多数方法已经被研究过，并且每种方法的优点和缺点都已经被彻底调查过，以确定它们的优点和缺点。基于人工智能（AI）的方法在解决SE问题方面具有良好的潜力，因此对其进行了专门研究。这类SE方法在解决未来的问题时是有益的。在此基础上，讨论了SE未来面临的挑战。作为一个案例研究，我们展示了人工智能技术，如迁移学习（TL），如何解决这些挑战之一，特别是在118总线系统中处理网络重构。这个例子可以指导那些对该领域感兴趣的人解决类似的挑战，并为未来的研究提供方向。

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引用次数: 0

Advanced convolutional neural networks for fiber impairments compensation in long haul optical communication systems 基于卷积神经网络的长距离光通信系统光纤损伤补偿

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-10-06 DOI: 10.1016/j.prime.2025.101123

Ali Hayder Abdul Kareem , Ibrahim A. Murdas

This paper proposes a convolutional neural network (CNN) based equalization scheme for mitigating fiber nonlinear impairments in high-capacity coherent optical communication systems. Unlike traditional digital back-propagation (DBP), the proposed CNN learns nonlinear signal distortions such as self-phase modulation (SPM), cross-phase modulation (XPM), and four-wave mixing (FWM) directly from data, enabling a balance between accuracy and computational efficiency. The model was trained and validated using co-simulation between OptiSystem and MATLAB over a 16-channel DWDM system with 16QAM and 64QAM modulation formats, achieving a total capacity of 1.92 Tb/s across 5000 km. By analyzing the performance metrics, it was gained insights into the effectiveness of the CNN algorithm in compensating for fiber impairments and optimizing signal transmission. The results showed the best value in terms of the quality of the received signal in 16QAM at 5 dBm to reach the Q-factor 11.45 dB with 0.087 for EVM, while in 64QAM at 10 dBm reach 11.09 dB and 0.09, respectively, that is larger than hard decision forward error correction HD-FEC limits (Q-factor =8.5 dB).

提出了一种基于卷积神经网络（CNN）的均衡方案，以减轻高容量相干光通信系统中的光纤非线性损伤。与传统的数字反向传播（DBP）不同，本文提出的CNN直接从数据中学习非线性信号畸变，如自相位调制（SPM）、交叉相位调制（XPM）和四波混频（FWM），从而实现了精度和计算效率之间的平衡。通过OptiSystem和MATLAB的联合仿真对该模型进行了训练和验证，该模型采用16QAM和64QAM调制格式的16通道DWDM系统，在5000 km范围内实现了1.92 Tb/s的总容量。通过分析性能指标，深入了解了CNN算法在补偿光纤损伤和优化信号传输方面的有效性。结果表明，在5dbm时，16QAM接收信号的质量最佳值达到了q因子11.45 dB， EVM为0.087，而在10dbm时，64QAM接收信号的q因子分别达到11.09 dB和0.09，这比硬判决前向纠错HD-FEC限值（q因子=8.5 dB）要大。

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引用次数: 0

Cloud behavior prediction for solar power applications: A bibliometric analysis, categorized literature review, and future research directions 太阳能应用的云行为预测：文献计量分析、分类文献综述及未来研究方向

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-10-06 DOI: 10.1016/j.prime.2025.101119

Maryam Nejati , Younes Mohammadi , Asghar Akbari Foroud , Thomas Olofsson

Accurate Cloud Behavior Prediction (CBP), also referred to as forecasting in this context, is essential for Solar Power Prediction (SPP), as well as for weather forecasting, climate analysis, and satellite imaging. However, the nonlinear and dynamic nature of clouds, combined with other limitations, presents significant challenges to advancing CBP. Recent developments, particularly the integration of Machine Learning (ML), Numerical Weather Prediction (NWP), and other innovative approaches, show strong potential for improving CBP and, in turn, enhancing SPP and related applications. This review presents a bibliometric analysis of 467 publications from 1970 to 2024, retrieved from the Scopus database using CBP-related keywords. It identifies trends, influential studies, major subject areas, leading authors, contributing countries, and key publishers. The study further categorizes the essential steps in CBP and provides a detailed review of the most relevant literature on cloud cover, cloud motion (including vector-based methods), and cloud image prediction. Additionally, it examines critical factors affecting model performance and introduces a framework for evaluating predictive methods based on input types, methodologies, prediction horizons, results, and evaluation metrics. Several key challenges are highlighted, including the nonlinearity of cloud behavior, limited data availability, image quality issues, and model accuracy. In response, actionable recommendations are offered, such as expanding data sources, applying hybrid imaging and modeling approaches, managing uncertainty, improving postprocessing techniques, and incorporating cloud content estimation. Given the relatively limited research in this field, this study serves as a valuable benchmark for researchers, engineers, and policymakers engaged in real-time SPP and other cloud-dependent domains.

准确的云行为预测（CBP），在这种情况下也被称为预报，对于太阳能预测（SPP）以及天气预报、气候分析和卫星成像至关重要。然而，云的非线性和动态特性，加上其他限制，对推进CBP提出了重大挑战。最近的发展，特别是机器学习（ML）、数值天气预报（NWP）和其他创新方法的整合，显示出改善CBP的强大潜力，进而增强SPP和相关应用。本综述使用与cbp相关的关键词对Scopus数据库中1970年至2024年的467篇出版物进行了文献计量学分析。它确定了趋势、有影响力的研究、主要学科领域、主要作者、贡献国家和主要出版商。该研究进一步对CBP的基本步骤进行了分类，并详细回顾了有关云量、云运动（包括基于向量的方法）和云图预测的最相关文献。此外，它还检查了影响模型性能的关键因素，并介绍了基于输入类型、方法、预测范围、结果和评估度量来评估预测方法的框架。强调了几个关键挑战，包括云行为的非线性、有限的数据可用性、图像质量问题和模型准确性。作为回应，提出了可操作的建议，如扩展数据源，应用混合成像和建模方法，管理不确定性，改进后处理技术，并结合云内容估计。鉴于该领域的研究相对有限，本研究为从事实时SPP和其他云依赖领域的研究人员、工程师和政策制定者提供了有价值的基准。

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引用次数: 0

Economic dispatch of diesel generators considering photovoltaic energy and thermal congestion in distribution networks in isolated areas 考虑光伏发电和偏远地区配电网热拥塞的柴油发电机组经济调度

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-10-04 DOI: 10.1016/j.prime.2025.101120

Carlos Arturo Páez , Didier Sierra , Isaac Dyner

The increase in extreme temperatures significantly affects electrical distribution networks, reducing both their transmission capacity and the efficiency of photovoltaic generation, thereby compromising operational security. In this context, the present study develops a computational model to evaluate the impact of ambient temperature on thermal congestion in power lines and on the efficiency of photovoltaic generation within the economic dispatch process of thermal generators. The model is formulated as a convex quadratic programming problem and implemented in Python using the IPOPT (Interior Point Optimizer) solver. It was applied to a case study in the city of Inírida, Colombia. The results indicate that the integration of distributed generation (DG) helps to mitigate thermal congestion in distribution networks by 6.91% and 12.10%, depending on the thermal conditions evaluated according to the IEEE 738 standard. Moreover, the efficiency of solar modules was found to decrease by 16.8% under elevated temperatures. Furthermore, operating costs were reduced by 36.7%, decreasing from USD 17,719.1 in the base scenario to USD 11,210.42 with the incorporation of distributed generation. Solar generation also contributed 7.9% of the total demand coverage, directly impacting the reduction of technical losses, which decreased from 553 kW to 362 kW. Similarly, a daily reduction in fuel consumption of 4,400.4 gallons and a reduction in CO₂ emissions of 43,641.4 kg were achieved. These findings demonstrate that the joint incorporation of climatic variables and renewable energy sources into the economic dispatch process enhances operational efficiency, improves the thermal resilience of the system, and promotes a more sustainable energy transition in isolated areas.

极端气温的升高严重影响了配电网，降低了其输电能力和光伏发电效率，从而危及运行安全。在此背景下，本研究开发了一个计算模型来评估环境温度对电力线路热拥塞和对火力发电机组经济调度过程中光伏发电效率的影响。该模型被表述为一个凸二次规划问题，并在Python中使用IPOPT（内部点优化器）求解器实现。它被应用于哥伦比亚Inírida市的一个案例研究。结果表明，根据IEEE 738标准评估的热状况，分布式发电（DG）的集成有助于缓解配电网的热拥塞，缓解率分别为6.91%和12.10%。此外，在高温下，太阳能组件的效率下降了16.8%。此外，运营成本降低了36.7%，从基本方案中的17,719.1美元下降到纳入分布式发电后的11,210.42美元。太阳能发电也贡献了总需求覆盖的7.9%，直接影响了技术损失的减少，从553千瓦下降到362千瓦。同样，每天减少燃料消耗4,400.4加仑，减少二氧化碳排放量43,641.4公斤。这些研究结果表明，将气候变量和可再生能源联合纳入经济调度过程可以提高运行效率，提高系统的热弹性，并促进偏远地区更可持续的能源转型。

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引用次数: 0

Optimized estimation of Li-ion battery parameters to improve the Enhanced Self-Correcting model with nonlinear least-squares data fitting and SoC estimation using Invariant EKF 基于非线性最小二乘数据拟合的锂离子电池参数优化估计和基于不变EKF的电池剩余电量估计

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-09-27 DOI: 10.1016/j.prime.2025.101115

Yassine Derouech, Abdelouahed Mesbahi

In recent years, batteries have become increasingly important, especially lithium batteries. Battery modeling is essential for many applications, such as state-of-charge (SoC) estimation. To obtain a high-performance model, the estimation of battery parameters must be precise. The Enhanced Self-Correcting model describes several battery dynamics, such as hysteresis cycling. This model can be further improved by making these parameters variable as a function of the SoC and temperature, making the model more efficient. In this study, the parameters of the single RC branch circuit during charging and discharging will be estimated, and these parameters will then be used to improve the “Enhanced Self-Correcting” model, which will enable it to better describe battery dynamics and improve accuracy. MATLAB/Simulink Toolboxes can simplify many tasks, and nonlinear least-squares data fitting using the Trust-Region-Reflective algorithm produces remarkable results. Then, this model is validated with the “SoC and voltage” estimation compared to other models, using the invariant extended Kalman filter (IEKF), which is reliable for nonlinear systems as its correction is independent of the output error, leading to greater accuracy and performance.

近年来，电池变得越来越重要，尤其是锂电池。电池建模对于许多应用都是必不可少的，例如充电状态（SoC）估计。为了获得高性能的模型，对电池参数的估计必须精确。增强型自校正模型描述了多种电池动力学，如迟滞循环。通过使这些参数随SoC和温度的变化而变化，可以进一步改进该模型，使模型更有效。在本研究中，将估算RC单支路在充放电过程中的参数，并将这些参数用于改进“Enhanced Self-Correcting”模型，使其能够更好地描述电池动态，提高准确性。MATLAB/Simulink工具箱可以简化许多任务，使用Trust-Region-Reflective算法的非线性最小二乘数据拟合效果显著。然后，与其他模型相比，使用不变扩展卡尔曼滤波（IEKF）验证了该模型的“SoC和电压”估计，该模型对于非线性系统是可靠的，因为它的校正与输出误差无关，从而提高了精度和性能。

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引用次数: 0

A triple-oxide SOI-MESFET with enhanced breakdown voltage and power density via buried aluminum engineering 一种通过埋铝工程提高击穿电压和功率密度的三氧化物SOI-MESFET

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2025-09-26 DOI: 10.1016/j.prime.2025.101117

Hamed Mohammadi, Mohsen Hayati

This study presents a novel triple-oxide SOI-MESFET structure with an engineered aluminum layer in the buried oxide (BOX) to simultaneously enhance breakdown voltage and power density. Through advanced TCAD simulations, we demonstrate a 44 % increase in breakdown voltage (from 17 V to 24 V) and 41.00 % higher maximum power density (0.739 W/mm vs 0.524 W/mm) compared to conventional designs. The strategic placement of three oxide regions in the channel optimizes electric field distribution, while the Al-BOX layer reduces parasitic capacitance and improves thermal stability. The device also exhibits excellent thermal stability, maintaining operation at ambient temperature under high bias conditions.

本研究提出了一种新型的三氧化物SOI-MESFET结构，在埋地氧化物（BOX）中加入工程铝层，同时提高击穿电压和功率密度。通过先进的TCAD模拟，我们证明了与传统设计相比，击穿电压增加44%（从17 V到24 V），最大功率密度提高41.00% （0.739 W/mm vs 0.524 W/mm）。在通道中战略性地放置三个氧化区优化了电场分布，而Al-BOX层减少了寄生电容并提高了热稳定性。该器件还具有优异的热稳定性，在高偏置条件下在环境温度下保持运行。

引用次数: 0